1002 papers
This collection explores the fascinating intersection where the laws of physics meet the complex machinery of chemistry. Here, researchers investigate how quantum mechanics governs molecular bonds, how light interacts with matter at the atomic scale, and how fundamental forces shape chemical reactions. It is a realm where abstract mathematical models collide with tangible substances to reveal the hidden mechanisms driving our material world.
On Gist.Science, we process every new preprint in this category directly from arXiv to make these discoveries accessible to everyone. Whether you are a seasoned expert or a curious reader, you will find both plain-language explanations and detailed technical summaries for each paper. Below are the latest contributions from the community pushing the boundaries of physical chemistry.
This paper introduces a variational nodal partition of correlation energy that defines static correlation as the energy penalty of constraining the wavefunction to mean-field nodes, thereby providing a rigorous, method-independent framework to distinguish static, dynamic, and strong correlation and explain the variable accuracy of fixed-node diffusion Monte Carlo.
This paper proposes a constructive, observation-centered framework for quantum mechanics that prioritizes signal analysis over traditional wave functions to address the mismatch between infinite-dimensional formalism and finite computational accuracy, ultimately aiming to provide rigorous effective descriptions for complex quantum systems.
This review examines the application of generative AI to the inverse design of inorganic compounds, analyzing how data-representation-model pipelines address unique challenges like symmetry and electronic structure across diverse systems while outlining future directions for benchmarking and synthesizability.
This paper presents a robust time-dependent density-functional theory approach within a time-evolution formalism to accurately model surface plasmon resonances in the continuum of aluminum and indium clusters, successfully capturing the transition from discrete spectral features to broad UV plasmons as cluster size increases.
The paper introduces XANE(3), an E(3)-equivariant graph neural network that accurately predicts X-ray absorption near-edge structure (XANES) spectra directly from atomic structures by combining tensor-product message passing with a derivative-aware training objective, achieving high fidelity in reproducing spectral features on a large iron oxide dataset.
This paper derives a leading-order semiclassical periodic orbit trace formula for local microcanonical out-of-time-order correlators (OTOCs) in systems with index-1 saddles, expressing the scrambling rate as a coherent sum over unstable periodic orbits on the Normally Hyperbolic Invariant Manifold (NHIM) to establish a theoretical mechanism for mode-selective control of quantum information scrambling.
This paper introduces a velocity-formulation alternative for hyper-Rayleigh scattering optical activity (HRS-OA) spectroscopy that, despite greater basis set dependence, ensures origin-independence by design, making it particularly suitable for calculations using approximated wavefunctions.
This paper introduces a hierarchical generative optimization framework that couples genetic algorithms with generative models to enable the automated, data-driven design of complex multi-component systems, successfully demonstrating a 30% reduction in activation barriers for catalytic environments through joint optimization of molecular composition and spatial arrangement.
This paper presents a numerically exact, symmetrized path-integral molecular dynamics method that utilizes an Eckart spring to rigorously project molecular systems onto specific rotational manifolds, enabling the efficient, simultaneous calculation of rotationally resolved tunneling splittings for multiple angular momentum states with results that closely match experimental trends and variational benchmarks.
This paper proposes a path-integral hybrid Monte Carlo method with enveloping bridging potentials (PIHMC-EBP) that enables numerically exact, efficient, and automated calculation of tunneling splittings in molecular systems, achieving unprecedented precision and reduced computational costs for molecules like malonaldehyde, the HCl dimer, and the water dimer.